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A novel bio-inspired approach with multi-resolution mapping for the path planning of multi-robot system in complex environments

Xin Yi, Anmin Zhu, Chaofan Li, Simon X. Yang

Year
2022
Citations
10
Access
Open access

Abstract

Abstract For multi-robot systems (MRSs), conventional path planning with single resolution mapping is challenging to balance information and computation. Regarding path planning of MRS, the previous research lacked systematic definition, quantitative evaluation, and the consideration of complex environmental factors. In this paper, a new systematic formulation is proposed to redefine the multi-robot path planning problem in complex environments, and evaluate the related solutions of this problem. To solve this problem, a novel bio-inspired approach based on reaction-diffusion system is given to deal with the path planning of MRS in complex environments, such as electromagnetic interference, ocean currents, and so on. Furthermore, a multi-layer neural dynamic network is proposed to describe environments with multiple resolutions, which can improve time performance while ensuring the integrity of environmental information. Comparative experimental results indicate that the proposed approach shows the excellent path planning performance of MRS in complex environments. The stability of the proposed method is determined by the mathematical basis.

Keywords

Motion planningComputer sciencePath (computing)RobotStability (learning theory)ComputationInterference (communication)Artificial intelligenceAlgorithmMachine learning

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